Bradley Simmons

Introducing STRATOS: A Cloud Broker Service

This paper introduces a cloud broker service (STRATOS) which facilitates the deployment and runtime management of cloud application topologies using cloud elements/services sourced on the fly from multiple providers, based on requirements specified in higher level objectives. Its implementation and use is evaluated in a set of experiments.

Exploring Alternative Approaches to Implement an Elasticity Policy

An elasticity policy governs how and when resources (e.g., application server instances at the PaaS layer) are added to and/or removed from a cloud environment. The elasticity policy can be implemented as a conventional control loop or as a set of heuristic rules. In the control-theoretic approach, complex constructs such as tracking filters, estimators, regulators, and controllers are utilized. In the heuristic, rule-based approach, various alerts(e.g., events) are defined on instance metrics (e.g., CPU utilization), which are then aggregated at a global scale in order to make provisioning decisions for a given application tier. This work provides an overview of our experiences designing and working with both approaches to construct an auto scaler for simple applications. We enumerate different criteria such as design complexity, ease of comprehension, and maintenance upon which we form an informal comparison between the different methods. We conclude with a brief discussion of how these approaches can be used in the governance of resources to better meet a high-level goal over time.

Distributed, application-level monitoring for heterogeneous clouds using stream processing

As utility computing is widely deployed, organizations and researchers are turning to the next generation of cloud systems: federating public clouds, integrating private and public clouds, and merging resources at all levels (IaaS, PaaS, SaaS). Adaptive systems can help address the challenge of managing this heterogeneous collection of resources. While services and libraries exist for basic management tasks that enable implementing decisions made by the manager, monitoring is an open challenge. We define a set of requirements for aggregating monitoring data from a heterogeneous collections of resources, sufficient to support adaptive systems. We present and implement an architecture using stream processing to provide near-realtime, cross-boundary, distributed, scalable, fault-tolerant monitoring. A case study illustrates the value of collecting and aggregating metrics from disparate sources. A set of experiments shows the feasibility of our prototype with regard to latency, overhead, and cost effectiveness.

Optimal autoscaling in a IaaS cloud

An application provider leases resources (i.e., virtual machine instances) of variable configurations from a IaaS provider over some lease duration (typically one hour). The application provider (i.e., consumer) would like to minimize their cost while meeting all service level obligations (SLOs). The mechanism of adding and removing resources at runtime is referred to as autoscaling. The process of autoscaling is automated through the use of a management component referred to as an autoscaler. This paper introduces a novel autoscaling approach in which both cloud and application dynamics are modeled in the context of a stochastic, model predictive control problem. The approach exploits trade-off between satisfying performance related objectives for the consumers application while minimizing their cost. Simulation results are presented demonstrating the efficacy of this new approach.

Feedback-based optimization of a private cloud

The optimization problem addressed by this paper involves the allocation of resources in a private cloud such that cost to the provider is minimized (through the maximization of resource sharing) while attempting to meet all client application requirements (as specified in the SLAs). At the heart of any optimization based resource allocation algorithm, there are two models: one that relates the application level quality of service to the given set of resources and one that maps a given service level and resource consumption to profit metrics. In this paper we investigate the optimization loop in which each applications performance model is dynamically updated at runtime to adapt to the changes in the system. These changes could be perturbations in the environment that had not been included in the model. Through experimentation we show that using these tracking models in the optimization loop will result in a more accurate optimization and thus result in the generation of greater profit.

Pattern-Based Deployment Service for Next Generation Clouds

This paper presents a flexible deployment service for cloud computing. The service facilitates the specification and the execution of cloud deployment plans for applications. An application is described through a pattern, an abstract view that captures the logical view of the application and its mapping into cloud resources. The services instantiate the pattern in the cloud and allows for runtime updates of the deployment. The service is accessible through a RESTful interface. We identify the requirements for the service, describe its interfaces and show several case studies that capture the main features of the service.

A Web Service for Cloud Metadata

Descriptive information about available cloud services (i.e., metadata) is required in order to make good decisions about which cloud service provider(s) to utilize when deploying an application topology to the cloud. Presently, there are no uniform mechanisms for describing these services. Further, there is no unifying process that aggregates this metadata from the set of cloud providers and makes it available to a user in a programmatic fashion from a single location. This paper presents a methodology for and an implementation of a service-oriented application that provides relevant metadata information describing offered cloud services via a uniform RESTful web service. The data provided by this service is automatically acquired and mapped to a standard ontology. Community members can submit performance benchmarks using a metrics agent that submits metrics via a web service. Several example applications using this API to help users select resources are presented.

A Decentralized Autonomic Architecture for Performance Control in the Cloud

In this paper, we introduce a decentralized autonomic architecture for multi-tier applications deployed in cloud environments. The architecture maintains the applications service level objective at a predefined level and, implicitly, reduces the cost. The architecture uses a series of autonomic controllers, in which each controller independently regulates a tier of the application. The architecture utilizes feedback loops and implements Proportional, Integrative and Derivative control laws at each autonomic controller. A prototype is described and an initial set of experiments is conducted on a public commercial cloud. The experiments demonstrate the effectiveness of this approach at maintaining a service level objective through the decomposition of an applications aggregate performance into its set of discretely managed component tiers.

Toward an Ecosystem for Precision Sharing of Segmented Big Data

As the amount of data created and stored by organizations continues to increase, attention is turning to extracting knowledge from that raw data, including making some data available outside of the organization to enable crowd analytics. The adoption of the MapReduce paradigm has made processing Big Data more accessible, but is still limited to data that is currently available, often only within an organization. Fine-grained control over what information is shared outside an organization is difficult to achieve with Big Data, particularly in the MapReduce model. We introduce a novel approach to sharing that enables fine-grained control over what data is shared. Users submit analytics tasks that run on infrastructure near the actual data, reducing network bottlenecks. Organizations allow access to a logical version of their data created at runtime by filtering and transforming the actual data without creating storage-intensive stale copies, and resellers can further segment or augment this data to provide added value to analytics tasks. A loosely-coupled ecosystem driven by web services allows for discovery and sharing with a flexible, secure environment that limits the knowledge those running analytics need to have about the actual provider of the data. We describe a proof-of-concept implementation of the various components required to realize this ecosystem, and present a set of experiments to demonstrate feasibility, showing advantageous performance versus storage trade-offs.

A runtime cloud efficiency software quality metric

This paper introduces the Cloud Efficiency (CE) metric, a novel runtime metric which assesses how effectively an application uses software-defined infrastructure. The CE metric is computed as the ratio of two functions: i) a benefit function which captures the current set of benefits derived from the application, and ii) a cost function which describes the current charges incurred by the applications resources. We motivate the need for the CE metric, describe in further detail how to compute it, and present experimental results demonstrating its calculation.